Decoupled DiLoCo 增强了分布式大语言模型预训练，打破了同步障碍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-23 08:45

研究人员开发了 Decoupled DiLoCo，一种新的分布式预训练框架，旨在提高大规模语言模型训练的弹性和效率。该方法超越了传统的 SPMD 范式，允许多个独立的“学习者”异步执行本地优化步骤。中央同步器然后使用最小法定人数和动态令牌加权合并来聚合参数更新，从而有效地绕过失败或缓慢的学习者，并消除全局停机时间。 AI

影响引入了一种更具弹性和效率的分布式训练方法，有可能减少大规模模型预训练的计算浪费和停机时间。

排序理由这是一篇描述新的分布式训练框架的研究论文。

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Jeff Dean · 2026-04-23 08:45

Decoupled DiLoCo for Resilient Distributed Pre-training

Modern large-scale language model pre-training relies heavily on the single program multiple data (SPMD) paradigm, which requires tight coupling across accelerators. Due to this coupling, transient slowdowns, hardware failures, and synchronization overhead stall the entire comput…

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Decoupled DiLoCo for Resilient Distributed Pre-training

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